OTN and DFO Activity Summary in Eastern Canada: DRAFT

proj_start <- ymd("20190101")
proj_end <- ymd("20230909")
proj_long_upp <- -40.00
proj_long_low <- -70.00
proj_lat_upp <- 60.00
proj_lat_low <- 40.00

geoserver_receivers <- readr::read_csv('https://members.oceantrack.org/geoserver/otn/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=otn:stations_receivers&outputFormat=csv', guess_max = 13579)

geoserver_tag_releases <- readr::read_csv('https://members.oceantrack.org/geoserver/otn/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=otn:animals&outputFormat=csv', guess_max = 13579)

geoserver_tag_releases <- geoserver_tag_releases %>%
  filter(yearcollected > 2018)

geoserver_projects <- readr::read_csv('https://members.oceantrack.org/geoserver/otn/ows?service=WFS&version=1.0.0&request=GetFeature&typeName=otn:otn_resources_metadata_points&outputFormat=csv', guess_max = 13579)

otn_stations <- geoserver_receivers %>%
  filter(!is.na(deploy_date)) %>%
  filter((deploy_date > proj_start & deploy_date < proj_end) |
         (recovery_date < proj_end & recovery_date > proj_start) |
         (deploy_date < proj_end & is.na(recovery_date) & deploy_date > proj_start - duration(18, 'months')) |
         (grepl('VR3', model) & deploy_date < proj_end & is.na(recovery_date) & deploy_date > proj_start - duration(4, 'years')) |
         (grepl('VR4', model) & deploy_date < proj_end & is.na(recovery_date) & deploy_date > proj_start - duration(6, 'years'))) %>%
  filter(stn_lat >= proj_lat_low & stn_lat <= proj_lat_upp &
         stn_long >= proj_long_low & stn_long <= proj_long_upp)

# Define the columns to keep
columns_to_keep <- c("FID", "institutioncode", "datacenter_reference", "scientificname", "vernacularname",
                      "longitude", "latitude", "basisofrecord", "yearcollected", "collector", "classname")

# Filter and select the desired columns
otn_animals <- geoserver_tag_releases %>%
  filter(longitude >= proj_long_low & longitude <= proj_long_upp &
         latitude >= proj_lat_low & latitude <= proj_lat_upp) %>%
  select(all_of(columns_to_keep))

# Define the columns to keep
columns_to_keep_projects <- c("FID", "resource_full_name", "ocean", "seriescode", "status", "collaborationtype")

# Filter and select the desired columns
otn_projects <- geoserver_projects %>%
   filter(seriescode == "DFOCanada") %>%
    select(all_of(columns_to_keep_projects))

library(leaflet)

# Create a Leaflet map
map <- leaflet(otn_stations) %>%
  addTiles()  # Add default basemap

# Add circle markers for each station
map <- map %>%
  addCircleMarkers(
    lng = ~stn_long,
    lat = ~stn_lat,
    label = ~paste(seriescode, instrumenttype, sep = " - "),
    radius = 0.5,  # Adjust the circle marker size as needed
    color = ~ifelse(seriescode == "DFOCanada", "red", "blue"),  # Marker color
    fillOpacity = 0.8  # Adjust fill opacity
  )

#Add bounding box based on filter
map <- map %>%
  addRectangles(
    lng1 = proj_long_low,  # Left longitude
    lat1 = proj_lat_low,   # Lower latitude
    lng2 = proj_long_upp,  # Right longitude
    lat2 = proj_lat_upp,   # Upper latitude
    weight = 2.5,            # Border width
    color = "darkorange",       # Border color
    fill = FALSE           # Don't fill the rectangle
  )

map <- map %>%
  addLegend(
    position = "bottomleft",
    colors = c("blue", "red", "darkorange"),
    labels = c("All OTN", "OTN-DFO", "Data filter"),
    title = "Legend"
    )

map <- map %>%
  addControl(
    html = sprintf("<strong>Filter:</strong> %s - %s", proj_start, proj_end)
  )

# Calculate the total number of receivers for each seriescode
summary_table <- otn_stations %>%
  group_by(seriescode) %>%
  summarize(
    Total_Receivers = n()) %>%
  mutate(Percentage_Relative_To_Total = round((Total_Receivers / sum(Total_Receivers)) * 100, 0))  # Calculate percentage and round

# Print table 
table_receivers<- knitr::kable(summary_table, 
                  format = "markdown",
                  align = "l", 
                  width = "100%")

# Display the map and table
map

table_receivers

# Create a summary table with counts of animals tagged by species
summary_otn_animals <- otn_animals %>%
  group_by(vernacularname) %>%
  summarise(Count = n(),
            Count_DFO = sum(grepl("DFO", institutioncode)))

# Reorder the levels of vernacularname based on Count in descending order
summary_otn_animals <- summary_otn_animals %>%
  arrange(desc(Count)) %>%
  mutate(vernacularname = factor(vernacularname, levels = vernacularname))

library(plotly)
plot_animals <- ggplot(summary_otn_animals, aes(x = vernacularname)) +
  geom_bar(aes(y = Count, fill = "OTN"), stat = "identity", position = "dodge") +
  geom_bar(aes(y = Count_DFO, fill = "OTN-DFO"), stat = "identity", position = "dodge") +
  labs(
    title = "Number of Animals Tagged by Species in Eastern Canada",
    x = "Species",
    y = "Number of Animals Tagged"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  coord_flip() +
  scale_fill_manual(values = c("OTN" = "blue", "OTN-DFO" = "red"))

# Convert the ggplot plot to a plotly object
interactive_plot <- ggplotly(plot_animals) %>%
  layout(legend = list(x = 0.5, y = 1))

# Create a Leaflet map named map_animals
map_animals <- leaflet(otn_animals) %>%
  addTiles()  # Add default basemap

# Add circle markers for each animal tagged
map_animals <- map_animals %>%
  addCircleMarkers(
    lng = ~longitude,
    lat = ~latitude,
    label = ~paste(institutioncode, scientificname, vernacularname, yearcollected, collector, sep = " - "),
    radius = 0.5,  # Adjust the circle marker size as needed
    color = ~ifelse(grepl("DFO", institutioncode), "red", "blue"),  # Marker color
    fillOpacity = 0.8  # Adjust fill opacity
  )

#Add bounding box based on filter
map_animals <- map_animals %>%
    addRectangles(
    lng1 = proj_long_low,  # Left longitude
    lat1 = proj_lat_low,   # Lower latitude
    lng2 = proj_long_upp,  # Right longitude
    lat2 = proj_lat_upp,   # Upper latitude
    weight = 2.5,            # Border width
    color = "darkorange",       # Border color
    fill = FALSE           # Don't fill the rectangle
  )

# Add legend
map_animals <- map_animals %>%
  addLegend(
    position = "bottomleft",
    colors = c("blue", "red", "darkorange"),
    labels = c("OTN", "OTN-DFO", "Data filter"),
    title = "Legend"
  )


# Create a text box to display min and max years
min_year <- min(otn_animals$yearcollected)
max_year <- max(otn_animals$yearcollected)
map_animals <- map_animals %>%
  addControl(
    html = sprintf("<strong>Year Collected Range:</strong> %d - %d", min_year, max_year),
    position = "topleft"
  )

map_animals

interactive_plot

knitr::kable(summary_otn_animals, format = "markdown", 
      col.names = c("Species", "OTN", "OTN-DFO"),
      caption = "Summary of Animals Tagged by Species in Eastern Canada")

stacked_bar_plot <- ggplot(otn_projects, aes(x = collaborationtype, fill = status)) +
  geom_bar() +
  labs(title = "Collaboration Type Distribution") +
  theme_minimal() +
  scale_fill_manual(values = c("ongoing" = "green", "completed" = "blue", "proposed" = "orange")) +
  coord_flip() 

# Convert the ggplot to a plotly object
interactive_stacked_bar_plot <- ggplotly(stacked_bar_plot)

interactive_stacked_bar_plot %>%
  layout(legend = list(x = 0.5, y = 1))

knitr::kable(otn_projects[, c("resource_full_name", "ocean", "status", "collaborationtype")],
      caption = "Summary of DFO-OTN Projects",
      format = "html",
      col.names = c("Project Title", "Ocean", "Status", "Collaboration Type"))